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Quadri, S. M. K.
- Software Architecture Evaluation: An Assessment
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Authors
Affiliations
1 Department of Computer Science, University of Kashmir, Jammu and Kashmir, IN
1 Department of Computer Science, University of Kashmir, Jammu and Kashmir, IN
Source
International Journal of System & Software Engineering, Vol 3, No 1 (2015), Pagination: 24-29Abstract
Software architecture is what defines a software system to be built. It starts early in the software development life cycle. The software architecture defines the data as well as the components of any software system along with the relation between them. It constitutes the blueprint that directs the development of the computer based software system. Being a critical activity of software development life cycle, any error in the design phase of software development can be critical to an organization dealing with the project and as such requires evaluation process that will not only analyse the architecture for its quality attributes but will benefit the software development organization by minimizing the risks associated with the software system to be built by pinpointing the errors early in the process of development. This paper highlights the architecture evaluation process with some examples of evaluation methods along with related work that has been previously done in the said field.Keywords
Architecture Analysis, Architecture Trade-off Analysis Method, ARGUS-I, Empiricallybased Software Architecture Evaluation, Layered Queuing Networks, Software Architecture Analysis Method, Software Evaluation Methods, Software Quality Attributes.
References
- Bahsoon, R., & Emmerich, W. (2003). Evaluating software architectures: Development stability and evolution. In Proceedings of the ACS/IEEE International Conference on Computer Systems and Applications, Tunis, Tunisia, (pp. 47-56.) IEEE Computer Society Press: Los Alamitos, US. Doi: 10.1109/ AICCSA.2003.1227480.
- Cook, D. (2007). Architecture evaluation and review practices. Retrieved from http://msdn.microsoft. com/en-us/library/bb896741.aspx
- Bass, L. (2007). Software architecture in practice. Pearson Education India.
- Mattson, M., Grahn, H., & Martensson, F. (2006). Software Architecture Evaluation Methods for Performance, Maintainability, Testability, and Portability. Retrieved from http://www.ide.bth. se/~hgr/Papers/qosa2006.pdf
- Pressman, R. S. (2005). Software Engineering: A Practitioner’s Approach (6th ed.). New York, NY: McGraw-Hill Higher Education.
- Roy, B., & Graham, T. C. N. (2008). Methods for Evaluating Software Architecture: A Survey. (Report No. 2008-545). School of Computing Queen’s University at Kingston: Canada. Retrieved from http://techreports.cs.queensu.ca/files/2008-545.pdf.
- Shaw, M., & Garland, D. (1996). Software architecture: Perspectives on an emerging discipline. USA: Prentice Hall.
- Shaw, M., & Clements, P. (2006). The golden age of software architecture: A comprehensive survey. Retrieved from http://reports-archive.adm.cs.cmu. edu/anon/isri2006/CMU-ISRI-06-101.pdf
- Clements, P., Klein, M., & Kazman, R. (2002). Evaluating software architectures: Methods and case studies. USA: Addison-Wesley Longman Publishing.
- Babar, M. A., Zhu, L., & Jeffery, R. (2004). A framework for classifying and comparing software architecture evaluation. In Proceedings of Australian Software Engineering Conference (ASWEC), (pp. 309-318). Retrieved from http://citeseerx.ist.psu.edu/viewdoc/ summary?doi=10.1.1.1.5504
- Griman, A., Perez, M., Mendoza, L., & Losavio, F. (2006). Feature analysis for architectural evaluation methods. Journal of Systems and Software, 79(6), 871-881. Doi: http://dx.doi.org/10.1016/j. jss.2005.12.015
- Li, Z. H. A. N. G., & Shou-Xin, G. H. W. (2008). Software architecture evaluation. Journal of Software.
- Breivold, H. P., Crnkovic, I., & Larsson, M. (2012). A systematic review of software architecture evolution research. Information and Software Technology, 54(1), 16-40.
- Samarthyam, G., Suryanarayana, G., Sharma, T., & S. Gupta. (2013). MIDAS: A design quality assessment method for industrial software. In Proceedings of the 2013 International Conference on Software Engineering, 2013, (pp. 911-920). Press Piscataway, NJ, USA.
- N. B. Harrison, & P. Avgeriou. (2013). Using PatternBased Architecture Reviews to Detect Quality Attribute Issues-An Exploratory Study. In J. Noble, R. Johnson, U. Zdun, & E. Wallingford (Eds.), Transactions on Pattern Languages of Programming III (pp. 168-194). Berlin Heidelberg: Springer. Doi: 10.1007/978-3-642-38676-3_5
- Roy, B., & Graham, T. C. N. (2008). Methods for evaluating software architecture: A survey. School of Computing TR, 545, 82.
- Mattsson, M., Håkan, G., & Frans, M. (2006). Software architecture evaluation methods for performance, maintainability, testability, and portability. In 2nd International Conference on the Quality of Software Architectures.
- Reusner, R., Schmidt, H. W., & Poernomo, I. H. (2003). Reliability prediction for component-based software architectures. Journal of Systems and Software, 66(3), 241-252.
- Ionita, M. T., Hammer, D. K., & Obbink, H. (2002). Scenario-Based Software Architecture Evaluation Methods: An Overview. Department Software Architectures, Philips Research, Mathematics and Computing Science, Technical University 2002. Book chapter: Evaluating Software Architecture.
- Vieira, M. E. R., Dias, M. S., & Richardson, D. J. (2000). Analyzing software architectures with Argus-I. In Proceedings of the 22nd International Conference on Software Engineering, (pp. 758-761).
- Petriu, D., Shousha, C., & Jalnapurkar, A. (2000). Architecture-based performance analysis applied to a telecommunication system. IEEE Transactions on Software Engineering, 26(11), 1049-1065.
- Lindvall, M., Tvedt, R. T., & Costa, P. (2003). An empirically based process for software architecture evaluation. Empirical Software Engineering, 8(1), 83-108.
- Identifying Some Problems with Selection of Software Testing Techniques
Abstract Views :213 |
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Authors
Affiliations
1 Department of Computer Sciences, University of Kashmir, Srinagar, IN
1 Department of Computer Sciences, University of Kashmir, Srinagar, IN
Source
Oriental Journal of Computer Science and Technology, Vol 3, No 2 (2010), Pagination: 265-268Abstract
Testing techniques refer to different methods or ways of testing particular features of a computer program, system or product. Presently there are so many different software testing techniques that we can use. Whether we decide to automate or just execute tests manually, there is a selection of testing techniques to choose from. We have to make sure that we select technique(s) that will help to ensure the most efficient and effective testing of the system. The fundamental problem in software testing thus throws an open question, as to what would be the techniques that we should adopt for an efficient and effective testing. Thus, the selection of right testing techniques at the right time for right problem will make the software testing efficient and effective. In this paper we discuss how should testing techniques be compared with one another and why do we face a problem in making appropriate testing technique selection.Keywords
Software Testing, Testing Techniques, Testing Techniques Selection.Full Text
- Some Notable Reliability Techniques for Disk File Systems
Abstract Views :166 |
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Authors
Affiliations
1 Department of Computer Sciences, University of Kashmir, Srinagar, IN
1 Department of Computer Sciences, University of Kashmir, Srinagar, IN
Source
Oriental Journal of Computer Science and Technology, Vol 3, No 2 (2010), Pagination: 269-271Abstract
File system operations include data operations and metadata operations. Data operations act upon actual user data while metadata operations modify the structure of the file system, like creating, deleting, or renaming files, directories, etc. During a metadata operation, the system must ensure that data are written to disk in such a way that the file system can be recovered to a consistent state after a system crash. In this paper we look at some most notable techniques which ensure reliability of disk file systems against system crashes and failures.Keywords
Disk File System, Reliability Techniques, Data Operations.Full Text
- Reliability Through Simulation:Goals and Limitations
Abstract Views :152 |
PDF Views:2
Authors
Affiliations
1 Department of Computer Sciences, University of Kashmir, J and K, IN
1 Department of Computer Sciences, University of Kashmir, J and K, IN
Source
Oriental Journal of Computer Science and Technology, Vol 4, No 1 (2011), Pagination: 135-140Abstract
Software Reliability is an important component of software quality. A number of software reliability models have been proposed since 1970s, but there is no single model that can be used in all the situations. To reduce the risk, it is better to experiment with the model of the system rather than with the system itself. Simulation, offers an attractive alternative to analytical models as it describes a system being characterized in terms of its artifacts, events, interrelationships and interactions in such a way that one may perform experiments on the model, rather than on the system itself. Simulation strives for achieving its goals but it does have certain limitations. This research paper focuses on the goals and limitations of using simulation in software reliability.Keywords
Simulation, Software Reliability, Model.Full Text
- Effectiveness of Software Testing Techniques on a Measurement Scale
Abstract Views :150 |
PDF Views:5
Authors
Affiliations
1 Department of Computer Sciences, University of Kashmir, Srinagar, IN
1 Department of Computer Sciences, University of Kashmir, Srinagar, IN
Source
Oriental Journal of Computer Science and Technology, Vol 3, No 1 (2010), Pagination: 109-113Abstract
Testing remains the truly effective means to assure the quality of a software system of nontrivial complexity. An important aspect of test planning is measuring test effectiveness. To make testing more successful we need to choose effective testing techniques. To compare testing techniques we need to place software testing techniques on a measurement scale which can define the relative merits of the existing testing techniques, but due to differences in software’s and its allied parameters this task seems to be complex, if not impossible.Keywords
Software Testing, Testing Techniques, Measurement Scales, Effectiveness.Full Text
- Review of FAT Data Structure of FAT32 File System
Abstract Views :141 |
PDF Views:4
Authors
Affiliations
1 Department of Computer Sciences, University of Kashmir, Srinagar, IN
1 Department of Computer Sciences, University of Kashmir, Srinagar, IN
Source
Oriental Journal of Computer Science and Technology, Vol 3, No 1 (2010), Pagination: 161-164Abstract
FAT file system is the most primitive, compatible and simple file system which still sustains in this era on digital devices, such as mini MP3 players, smart phones, and digital cameras. This file system is supported by almost all operating systems because of its simplicity and legacy. This paper presents review of the basic design technique, constraints and formulas of the most important and building block data structure of FAT32 file system; the FAT data structure.Keywords
FAT, File Allocation Table, FAT32, File System, Cluster, Sector.Full Text
- Software Cost Estimation Based on the Hybrid Model of Input Selection Procedure and Artificial Neural Network
Abstract Views :207 |
PDF Views:5
Authors
Affiliations
1 Department of Computer Science, University of Kashmir, J&K, IN
2 Department of Computer Science and Hony. Addl., FTK-Centre for Information Technology, Jamia Millia Islamia, Central University, Jamia Nagar, New Delhi, IN
1 Department of Computer Science, University of Kashmir, J&K, IN
2 Department of Computer Science and Hony. Addl., FTK-Centre for Information Technology, Jamia Millia Islamia, Central University, Jamia Nagar, New Delhi, IN
Source
Artificial Intelligent Systems and Machine Learning, Vol 10, No 1 (2018), Pagination: 18-24Abstract
Software effort estimation is the forecasting of development effort and development time needed to develop any software project. It is considered to be the very primary step of software development process and at the same time considered to be the key task as accurate assessments of growth of the current project, its delivery exactness and its cost control can only be achieved once desired estimation is accurate. And at broader perspective an accurate estimation of a currently developing software product will result in landing the organization in a better schedule of its futuristic software projects too. With due above reason, software effort estimation has received a considerable amount of attention of many researchers from past so many decades. In this paper, software cost estimation is done by first performing a proposed input selection procedure to get the relevant set of cost drivers and leaving behind the irrelevant attributes. In the next step, it is now only these relevant set of attributes that are being assigned to Artificial Neural Network as its input for the purpose of getting the accurate estimation of software development effort and cost. Removing the irrelevant cost drivers at the very first step directly leads to attain accurate software cost estimation results. Besides this the proposed model results in a significant decrease of complexities associated with traditional Artificial Neural Network based Software cost estimation models. For the purpose of evaluation of proposed model, Magnitude of Relative Error and Median of Magnitude of Relative Error are used as a measure of performance index to weigh the obtained quality of estimation which becomes more evident when later compared with two existing models. After an extensive evaluation of results, it showed that the proposed model performs well in software cost estimation.Keywords
Artificial Neural Network, Functional Link Artificial Neural Network, Genetic Algorithms, Input Selection Procedure, Software Cost Estimation.References
- B.W. Boehm, Software Engineering Economics, Prentice-Hall, Englewood Cliffs, New Jersy, 1981.
- B.W. Boehm, Software Cost Estimation with COCOMO II, Prentice Hall PTR, Englewood Cliffs, New Jersy, 2000.
- Kitchenham, B., Pickard, L.M., Linkman, S., Jones, P.W., 2003. Modeling software bidding risks. IEEE Transactions on Software Engineering 29 (6), 542-554.
- Fenton, N.E., Pleeger, S.L., 1997. Software Metrics: A Rigorous and Practical Approach, second ed. PWS, Boston, MA, USA.
- Pressman, R.S., 1997. Software Engineering: A Practitioner’s Approach, fourth ed. McGraw-Hill, New York, NY, USA.
- Miyazaki, Y., Terakado, Y., Ozaki, K., Nozaki, N., 1994. Robust regression for developing Software estimation models. Journal of System and Software 27 (1), 16-35.
- Gray, A.R., 1999. A simulation-based comparison of empirical modeling techniques for software metric models of development effort. In: Proceedings of ICONIP, Sixth International Conference on Neural Information Processing, Perth, WA, Australia, vol. 2, pp. 526-531.
- Kemerer, C., 1987. An empirical validation of software cost estimation models. Communications of the ACM 30 (5), 416-429.
- Verkatachalm, A.R., 1993. Software cost estimation using artificial neural networks. International Joint Conference on Neural Networks, vol. 1.IEEE, Nagoya, pp. 987-990.
- Xu, Z., Khoshgoftaar, T.M., 2004. Identification of fuzzy models of cost estimation. Fuzzy Sets and Systems 145, 141-163.
- Hughes, R.T., 1996. An evaluation of machine learning techniques for software effort estimation. University of Brighton.
- Jorgerson, M., 1995. Experience with accuracy of software maintenance task effort prediction models. IEEE Transaction on Software Engineering 21 (8), 674-681.
- Samson, B., Ellison, D., Dugard, P., 1997. Software cost estimation using an Albus perceptron (CMAC). Information and Software Technology 39 (1), 55-60.
- Heiat, A., 2002. Comparison of artificial neural network and regression models for estimating software development effort. Information and Software Technology 44 (15), 911-922.
- Seluca, C., 1995. An investigation into software effort estimation using a back-propagation neural network, M.Sc.Thesis, Bournemouth University,UK.
- Srinivasan, K., Fisher, D., 1995. Machine learning approaches to estimating software development effort. IEEE Transaction on Software Engineering 21 (2), 126-136.
- Wittig, G., Finnie, G., 1997. Estimating software development effort with connectionist models. Information and Software Technology 39 (7),469-476.
- Schofield, C., 1998. Non-algorithmic effort estimation techniques. Technical Report TR98-01.
- Chiu, N.H., Huang, S.J., 2007. The adjusted analogy-based software effort estimation based on similarity distances. Journal of Systems and Software 80 (4), 628-640.
- Vinay Kumar K.; Ravi V.; Mahil Carr; Raj Kiran N., (2008). Software development cost estimation using wavelet neural networks, The Journal of Systems and Software 81(11), 1853-1867.
- Tirimula Rao B.; Sameet B.; Kiran Swathi G.; Vikram Gupta K.; Ravi Teja;Ch, Sumana S., (2009), A Novel Neural Network Approach for Software Cost Estimation using Functional Link Artificial Neural Network (FLANN), International Journal of Computer Science and Network Society 9(6), 126-131.
- Reddy C.S.; Raju KVSN, (2009). An Improved Fuzzy Approach for COCOMO’s Effort Estimation using Gaussian Membership Function. Journal of software 4(5), 452-459.
- F.S. Gharehchopogh, “Neural Networks Application in Software Cost Estimation: A Case Study”, 2011 International Symposium on Innovations in Intelligent Systems and Applications (INISTA 2011), pp. 69-73, IEEE, Istanbul, Turkey, 15-18 June 2011.
- A.F. Sheta, “Estimation of the COCOMO Model Parameters Using Genetic Algorithms for NASA Software Projects”, Journal of Computer Science, Vol. 2, No. 2, pp.118-123, 2006.
- Ziauddin, Sh. K.Tipu, Kh. Zaman, Sh. Zia, “Software Cost Estimation Using Soft Computing Techniques”, Advances in Information Technology and Management (AITM), Vol. 2, No. 1, pp. 233-238, 2012.
- Mair, G. Kadoda, M. Lefley, K. Phalp, Ch. Schofield, M. Shepperd, S. Webster, “An Investigation of Machine Learning Based Prediction Systems”, Journal of systems and software, Elsevier, Vol. 53, Issue 1, pp. 23-29, 2000.
- Prasad Reddy P.V.G.D, Hari CH.V.M.K, S. Rao.T, “Multi Objective Particle Swarm Optimization for Software Cost Estimation”, International Journal of Computer Applications, Vol. 32, No.3, pp. 13-17, October 2011.
- Z.A. KHALIFELU, F.S. GHAREHCHOPOGH, "Comparison and Evaluation Data Mining Techniques with Algorithmic Models in Software Cost Estimation", Elsevier, Procedia-Technology Journal, Vol. 1, pp. 65-71, 2012.
- Mair, M. Shepperd, and M. Jorgensen, “An Analysis of Datasets Used to Train and Validate Cost Prediction Systems,” ACM SIGSOFT Software Eng. Notes, vol. 4, pp. 1-6, 2005.
- T. Menzies, Z. Chen, J. Hihn, and K. Lum, “Selecting Best Practices for Effort Estimation,” IEEE Trans. Software Eng., vol. 32, no. 11, pp. 883-895, Nov. 2006.
- S.D. Conte, H.E. Dunsmore, and V.Y. Shen, Software Engineering Metrics and Models. The Benjamin/Cummings Publishing Company, Inc., 1986.
- Port and M. Korte, “Comparative Studies of the Model Evaluation Criterions MMRE and PRED in Software Cost Estimation Research,” Proc. Second ACM-IEEE Int’l Symp. Empirical Software Eng. and Measurement, pp. 51-60, Oct. 2008.
- B. Tirimula Rao, B. Sameet, G. Kiran Swathi, K. Vikram Gupta, Ch. RaviTeja, S.Sumana, “A Novel Neural Network Approach For Software Cost Estimation Using Functional Link Artificial Neural Network (FLANN), ” IJCSNS International Journal of Computer Science and Network Security, Vol. 9, No.6, June 2009.
- Mapping Cloud-Based Facilities for Academic Use
Abstract Views :163 |
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Authors
Affiliations
1 University of Kashmir, Hazratbal, Srinagar, Jammu & Kashmir, IN
2 Department of Computer Science, Jamia Millia Islamia, New Delhi, IN
1 University of Kashmir, Hazratbal, Srinagar, Jammu & Kashmir, IN
2 Department of Computer Science, Jamia Millia Islamia, New Delhi, IN
Source
International Journal of Distributed and Cloud Computing, Vol 6, No 1 (2018), Pagination: 19-28Abstract
The pay-as-you-use service model is one of the key factors for the success of cloud computing paradigm: resources are used only when needed and charged on basis of their actual usage. There are ICT services through cloud which are even provided free to educational institutions and institutions can benefit from these services. Universities necessarily do not have enough resources to spend on subscribing or establishing ICT facilities but these facilities are essential for improving the way faculty & student interact, communicate, and carry research activities. Faculty, staff and students have access to services like institutional email, 1 TB drive space, Office applications free which otherwise require purchased licenses. In this paper, the different free services provided for educational use are discussed, analysed for perception of its adoption. Further, case study of these services implemented at university of Kashmir has been chosen as the research methodology to discuss and demonstrate the educational cloud services. According to the results, clear understanding and operational ease, high reliability, ease of accessibility with no financial implications is achieved. This guarantees the desired performance level and minimises the expenditure otherwise to be incurred on setting up or subscribing to such services.Keywords
Cloud Computing, Cloud Services, Educational Cloud, Hybrid Computing, Mapped Computing, Universities.References
- A. K. Gupta, and G. Kaur, “Benefits and security issues in cloud computing,” International Journal of Emerging Research in Management & Technology, vol. 2, no. 1, pp. 108-114, 2013.
- A. Aleem, and C. R. Sprott, “Let me in the cloud: analysis of the benefit and risk assessment of cloud platform,” Journal of Financial Crime, vol. 20, no. 1, pp. 6-24, 2012.
- R. Buyya, C. S. Yeo, S. Venugopal, J. Broberg, and I. Brandic, “Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility,” Future Generation Computer Systems, vol. 25, no. 6, pp. 599-616, 2009.
- C. Cadregari, and A. Cutaia, “Every silver cloud has a dark lining: A primer on cloud computing, regulatory and data security risk,” ISACA Journal, vol. 3, no. 12, pp. 1-4, 2011.
- D. Catteddu, and G. Hogben, “Cloud computing: Benefit, risks and recommendations for information security,” European Network and Information Security Agency (ENISA), pp. 1-125, 2009.
- C. Mukundha, C., P. Gayathri, and I. S. Prabha, “Load balance scheduling algorithm for serving of requests in cloud networks using software defined networks,” International Journal of Applied Engineering Research, vol. 11, no. 6, pp. 3910-3914, 2016.
- D. C. Wyld, “The cloudy future of government IT: Cloud computing and the public sector around the world,” International Journal of Web & Semantic Technology, vol. 1, no. 1, pp. 1-20, 2010.
- D. K. Gupta, A. Mishra, and G. Sahoo, “Cloud computing: Solving availability problem in future framework for e-governance,” International Journal of Computer Applications & Information Technology, vol. 2, no. 11, pp.16-20, 2013.
- D. H. Shin, “User centric cloud service model in public sectors: Policy implications of cloud services,” Government Information Quarterly, vol. 30, no. 2, pp. 194-203, 2013.
- T. Ercan, “Effective use of cloud computing in educational institutions,” Procedia-Social and Behavioral Sciences, vol. 2, no. 2, pp. 938-942, 2010.
- B. Furht, B. (2010). Cloud computing fundamentals. In Handbook of cloud computing, US: Springer, pp. 3-19, 2010.
- A. Goscinski, and M. Brock, “Toward dynamic and attribute based publication, discovery and selection for cloud computing,” Future Generation Computer Systems, vol. 26, no. 7, pp. 947-970, 2010.
- R. L. Grossman, “The case for cloud computing,” IT Professional, vol. 11, no. 2, pp. 23-27, 2009.
- S. Hashemi, K. Monfaredi, and M. Masdari, “Using cloud computing for e-government: Challenges and benefits,” International Journal of Computer, Information, Systems and Control Engineering, vol. 7, no. 9, pp. 596-603, 2013.
- A. Hussein, and O. Mohamed, “Cloud computing and its effect on performance excellence at higher education institutions in Egypt (An analytical study),” European Scientific Journal, vol. 11, no. 10, pp. 163-176, 2015.
- I. Kaur, and K. Bala, “E-governance: Benefits and challenges of cloud based architecture,” International Journal of Computer Science and Technology, vol. 6, no. 3, pp. 35-37, 2015.
- K. Mukherjee, and G. Sahoo, “Cloud computing: Future framework for e-governance,” International Journal of Computer Applications, vol. 7, no. 7, pp. 31-34, 2010.
- KPMG. “The Cloud changing the business ecosystem: Survey report,” Available https://www.kpmg.com/IN/en/IssuesAndInsights/ThoughtLeadership/The_Cloud_Changing_the_Business_Ecosystem.pdf (accessed 13 November, 2016).
- K. Vats, S. Sharma, and A. Rathee, “A review of cloud computing and e-governance,” International Journal of Advanced Research in Computer science and Software Engineering, vol. 2, no. 2, 2012.
- M. Dash, and R. N. Panda, “Cloud in Indian e-governance model,” International Journal of Recent Development in Engineering and Technology, vol. 2, no. 2, pp. 13-15, 2014.
- S. Marston, Z. Li, S. Bandyopadhyay, J. Zhang, and A. Ghalsasi, “Cloud computing- The business perspective,” Decision Support Systems, vol. 51, no. 1, pp. 176-189, 2011.
- M. Yuvaraj, Problems and prospects of implementing cloud computing in university libraries: A case study of Banaras Hindu University library system,” Library Review, vol. 64, no. 8/9, pp. 567-582, 2015.
- P. Mell, and T. Grance, “The NIST definition of cloud computing,” Available http://nvlpubs.nist.gov/nistpubs/Legacy/SP/nistspecialpublication800-145.pdf (accessed 11 November, 2016).
- Microsoft. “Cloud Computing: What is Infrastructure as a Service,” TechNet Magazine 2011, Available https://technet.microsoft.com/en-us/library/hh509051.aspx (accessed 12 November, 2016).
- N. Sultan, Cloud computing for education: A new dawn?” International Journal of Information Management, vol. 30, no. 2, pp. 109-116, 2010.
- P. Samimi, Y. Teimouri, and M. Mukhtar, “A combinatorial double action resource allocation model in cloud computing,” Information Sciences, vol. 357, no. C pp. 201-216, 2016.
- R. Prodan, and S. Ostermann, “A survey and taxonomy of infrastructure as a service and web hosting cloud providers,” In 10th IEEE/ACM International Conference on Grid Computing, pp. 17-25, 2009.
- E. M. N. Qadri, E-governance at University of Kashmir: Bringing efficiency & transparency, International Journal of Information and Computation Technology, vol. 4, no. 2, pp. 119-126, 2014.
- Q. Zhang, L. Cheng, and R. Boutaba, “Cloud computing: State-of-the-art and research challenges,” Journal of Internet Services and Applications, vol. 1, no. 1, pp. 7-18, 2010.
- L. Qian, Z. Luo, Y. Du, and L. Guo, “Cloud computing: An overview,” Cloud Computing, vol. 5931, pp. 626-631, 2009.
- V. Rajaraman, “Cloud computing,” Resonance, vol. 19, no. 3, pp. 242-258, 2014.
- S. M. K. Quadri, M. Zaman, and M. N. Qadri, E-governance development: Agile Perspective, 2014.
- S. Okai, M. Uddin, A. Arshad, R. Alsaqour, and A. Shah, “Cloud computing adoption model for universities to increase ICT proficiency,” SAGE Open, vol. 4, no. 3, pp. 1-10, 2014.
- S. Paquette, P. T. Jaeger, and S. C. Wilson, “Identifying the security risks associated with governmental use of cloud computing,” Government Information Quarterly, vol. 27, no. 3, pp. 245-253, 2010.
- Smitha, K. K., Thomas, T., & Chitharanjan, K. (2012). Cloud based e-governance system: A survey. Procedia Engineering, 38, 3816-3823.
- P. Y. Thomas, “Cloud computing: A potential paradigm for practicing the scholarship of teaching and learning,” The Electronic Library, vol. 29, no. 2, pp. 214-224, 2011.
- J. Voas, and J. Zhang, “Cloud computing: New wine or just a new bottle?” IT Professional, vol. 11, no. 2, pp. 15-17, 2009.
- L. Wang, G. V. Laszewski, A. Younge, X. He, M. Kunze, J. Tao, and C. Fu, “Cloud computing: A perspective study,” New Generation Computing, vol. 28, no. 2, pp. 137-146, 2010.
- W. W. Wu, L. W. Lan, and Y. T. Lee, “Factors hindering acceptance of using cloud services in university: A case study,” The Electronic Library, vol. 31, no. 1, pp. 84-98, 2013.
- W. Lin, J. Z. Wang, C. Liang, and D. Qi, “A threshold-based dynamic resource allocation scheme for cloud computing,” Procedia Engineering, vol. 23, pp. 695-703, 2011.
- W. W. Wu, “Mining significant factors affecting the adoption of SaaS using the rough set approach,” Journal of Systems and Software, vol. 84, no. 3, pp. 435-441, 2011.
- W. W. Wu, L. W. Lan, and Y. T. Lee, “Exploring decisive factors affecting an organization’s SaaS adoption: A case study,” International Journal of Information Management, vol. 31, no. 6, pp. 556-563, 2011.
- M. Yuvaraj, Cloud computing software and solutions for libraries: A comparative study,” Journal of Electronic Resources in Medical Libraries, vol. 12, no. 1, pp. 25-41, 2015.